MAP-5.2: Practices and personnel for supporting regular engagement with relevant AI actors and integrating feedback about positive, negative, and unanticipated impacts are in place and documented.

To meet MAP-5.2, you need a documented, staffed process that regularly engages relevant AI actors (internal teams, impacted users, third parties, and other stakeholders) and reliably turns their feedback into tracked actions across the AI lifecycle. The control is “operating” only if feedback changes decisions, risks, and release gates, and you can prove it. ¹

Key takeaways:

• Assign named roles and a repeatable cadence for AI stakeholder engagement, not ad hoc outreach. ¹
• Standardize intake, triage, and escalation so positive, negative, and unanticipated impacts become managed risk items. ¹
• Retain evidence that feedback influenced requirements, monitoring, incident response, and model updates. ¹

MAP-5.2 sits in the “MAP” function of the NIST AI Risk Management Framework and pushes you toward a disciplined stakeholder engagement loop: identify relevant AI actors, engage them regularly, collect structured feedback, and integrate that feedback into risk decisions and operational controls. The requirement is short, but the operational bar is not. A CCO or GRC lead should treat MAP-5.2 as a governance and evidence problem: who owns engagement, what “regular” means in your organization, how you capture feedback beyond a generic inbox, and how you prove it made it into the backlog, risk register, change control, and monitoring plans.

This requirement becomes urgent when you deploy AI into customer-facing workflows, employment decisions, credit or pricing, healthcare, safety-relevant environments, or any domain where unexpected impacts show up after launch. It also applies when you rely on third parties for models, data, labeling, or evaluation. “Engagement” must include the humans closest to real-world harms and benefits, not only your engineering leadership. The target outcome is defensible: you can show a reviewer that you don’t learn about harms by accident; you have an operating mechanism to hear them early and act on them. ²

Regulatory text

Excerpt (MAP-5.2): “Practices and personnel for supporting regular engagement with relevant AI actors and integrating feedback about positive, negative, and unanticipated impacts are in place and documented.” ¹

What the operator must do:
You must (1) define who the “relevant AI actors” are for each AI system, (2) set up documented engagement practices with a recurring cadence, (3) assign personnel accountable for running those practices, and (4) integrate feedback into your AI risk management and system lifecycle in a traceable way. A policy statement alone is not enough; auditors will look for operating records that show feedback moving into decisions and control updates. ¹

Plain-English interpretation

MAP-5.2 is a closed-loop control: listen, decide, act, and document.

• Listen: establish channels to gather feedback from impacted groups and internal operators.
• Decide: triage the feedback into risk, product, compliance, or incident workflows.
• Act: implement mitigations (model changes, UI changes, constraints, monitoring, or user communications).
• Document: keep evidence linking feedback to outcomes. ¹

The key phrase is “positive, negative, and unanticipated impacts.” If your process only captures complaints, you miss beneficial outcomes that should be scaled and unanticipated impacts that signal blind spots in testing and evaluation. ¹

Who it applies to

Entities: Organizations developing or deploying AI systems, including those that build in-house models, fine-tune third-party models, or embed AI features from third parties into products and operations. ¹

Operational contexts where MAP-5.2 is exam-relevant:

• Production AI systems with end-user or customer impact (recommendations, personalization, automated assistance, decision support).
• High-impact decision workflows where AI influences eligibility, access, ranking, or prioritization.
• Third-party AI dependencies (model APIs, foundation models, data brokers, labeling firms) where external actors can surface issues you cannot observe internally.
• Internal AI (HR, security, finance) where employees and candidates become impacted parties.

What you actually need to do (step-by-step)

1) Name the AI actors per system (stakeholder map)

Create a system-level “AI Actor Register” and keep it current:

• Internal actors: model owners, product owners, data owners, MLOps, customer support, security, legal/compliance, privacy, procurement, internal audit.
• External actors: end users, customers, impacted non-users (when relevant), regulators (where applicable), advocacy groups or domain experts (risk-based), and third parties in the AI supply chain.
  Output: one page per AI system listing actor categories, contact methods, and engagement frequency. ¹

2) Assign personnel and accountability (RACI)

For each AI system, assign:

• Engagement owner: runs recurring sessions, maintains the intake queue, ensures follow-through.
• Risk owner: accepts/mitigates risk items and signs off on risk decisions.
• Business owner: accountable for product/process outcomes and resources.
• Escalation roles: legal/compliance, privacy, security, incident response.
  A simple RACI table per system is usually sufficient if it is used in practice. ¹

3) Define “regular engagement” as a control requirement

Write a short procedure that states:

• Engagement triggers (new model, material change, new use case, incident, sustained complaints, monitoring anomaly).
• Engagement modes (user research, advisory councils, support ticket tagging, internal operator roundtables, third-party QBRs).
• Minimum expectations for cadence (define it per risk tier rather than one-size-fits-all).
• Documentation and retention rules. ¹

4) Standardize feedback intake (so you can audit it)

Build a structured intake process that captures:

• AI system name/version, context of use, reporter type, summary, evidence (screenshots/logs), impact type (positive/negative/unanticipated), severity, and time sensitivity.
• Whether the issue indicates potential safety, discrimination, privacy, security, or consumer harm (your taxonomy).
  Practical approach: integrate with your existing ticketing system, GRC issue management, or a dedicated AI risk intake form. ¹

5) Triage and route feedback into the right workflow

Define routing rules:

• Potential harm or legal/compliance exposure: create a risk item, link to controls, and set owner and due date.
• Possible incident: invoke incident intake and preservation (logs, prompts, outputs).
• Product improvements: create backlog items with traceability to the original feedback.
• Model performance drift: route to monitoring/evaluation owners for investigation. ¹

6) Integrate feedback into lifecycle gates (make it change decisions)

Tie feedback outcomes to:

• Pre-release reviews: launch readiness includes open feedback items and mitigation status.
• Change management: model updates require review of recent feedback trends.
• Post-deployment monitoring: new monitors or thresholds added due to observed impacts.
• Third-party management: feedback about third-party components becomes part of third-party reviews and contract governance. ¹

7) Document decisions and close the loop with stakeholders

Track for each material feedback item:

• Decision (accept risk, mitigate, retire feature, constrain use).
• Rationale and approvers.
• Remediation steps and verification.
• Communication back to the reporting channel where appropriate.
  Auditors like to see closure evidence, not only intake volume. ¹

Required evidence and artifacts to retain

Keep artifacts in a way that a reviewer can trace feedback → decision → action → verification:

Core governance

• MAP-5.2 policy/standard statement mapped to an operating procedure. ¹
• System-level AI Actor Register and engagement plan.
• RACI / control ownership assignment.

Operating evidence

• Meeting agendas/minutes for engagement forums (internal and external where appropriate).
• Ticket extracts or intake logs showing classification (positive/negative/unanticipated) and routing.
• Risk register entries linked to feedback items and model versions.
• Change control records showing mitigations implemented (feature flags, guardrails, retraining, UI changes).
• Monitoring updates or evaluation reports created because of feedback.

Third-party evidence (if applicable)

• Third-party QBR notes where AI impacts are discussed.
• Contractual obligations or SLAs supporting feedback handling (escalation, incident notice, model change notice).

Common exam/audit questions and hangups

Expect questions like:

• “Who are your relevant AI actors for this system, and how did you decide?”
• “Show me the last two cycles of engagement and the outputs.”
• “Where does feedback live, and who triages it?”
• “Give an example where feedback changed a model, a control, or a deployment decision.”
• “How do you capture unanticipated impacts beyond customer complaints?”
  Hangup: teams provide a policy and a slide deck, but no traceable operational records. MAP-5.2 is evidence-driven. ¹

Frequent implementation mistakes (and how to avoid them)

1. Only engaging internal stakeholders.
   Fix: require at least one external signal per system (support channels, user research, customer councils, or partner feedback), scaled by risk. ¹

2. Feedback goes to a shared inbox with no triage owner.
   Fix: name a primary and backup engagement owner and enforce SLAs for triage in your procedure.

3. No taxonomy for “unanticipated impacts.”
   Fix: add a required intake field for “unexpected outcome type” plus free text, then review trends in governance meetings. ¹

4. No linkage to model versioning and change control.
   Fix: require model/version identifiers in intake and require closure notes to reference the release or configuration change that addressed it.

5. Third parties are excluded from the loop.
   Fix: add contract hooks for escalation paths and require that third-party AI providers participate in periodic reviews when they materially affect outcomes.

Enforcement context and risk implications

NIST AI RMF is a framework, not a regulator, so MAP-5.2 is not “enforced” by NIST in the way a statute is. Your exposure comes from downstream expectations: if you cannot demonstrate stakeholder engagement and feedback integration, you are more likely to miss harmful impacts, mishandle complaints, and fail internal governance reviews. The operational risk is predictable: issues surface in production first, then legal and reputational response becomes your control environment. Treat MAP-5.2 as a preventive control that reduces surprise. ³

A practical 30/60/90-day execution plan

First 30 days (stand up the control skeleton)

• Appoint an executive sponsor and a MAP-5.2 control owner per major AI system. ¹
• Draft the engagement procedure: actor identification, cadence definition, intake fields, triage routing, and escalation criteria. ¹
• Create the AI Actor Register template and complete it for your highest-risk AI system.
• Implement a single intake mechanism (ticket form or GRC workflow) with required fields for impact type and model/version.

Days 31–60 (operate it on one system and fix friction)

• Run at least one structured engagement cycle (internal operator roundtable plus an external signal review such as support tickets or user research outputs).
• Triage all received feedback; create linked risk items or backlog items with owners and target dates.
• Add a release gate: deployment cannot proceed if certain categories of feedback remain untriaged or unowned.
• Conduct a short retrospective: what feedback categories recur, where routing fails, what evidence is missing.

Days 61–90 (scale to portfolio and make it audit-ready)

• Roll the control to additional AI systems based on risk tiering.
• Standardize reporting: a recurring dashboard showing feedback volumes by type, aging, and closure outcomes (qualitative is fine; avoid vanity metrics).
• Embed third-party participation for relevant providers (standing agenda item in QBRs).
• Prepare an audit packet: procedure, actor registers, sample engagement minutes, and two or three traced examples from feedback to remediation.

Where Daydream fits: Daydream can serve as the system of record for mapping MAP-5.2 to a policy, procedure, control owner, and recurring evidence collection, which reduces scramble during audits and makes “operating effectiveness” easier to demonstrate. ¹

Frequently Asked Questions

What counts as “relevant AI actors” for MAP-5.2?

Start with anyone who builds, operates, is materially impacted by, or supplies components to the AI system. Document your rationale per system and update it when the use case or deployment context changes. ¹

How do we prove we “integrated” feedback instead of just collecting it?

Maintain traceability from each material feedback item to a decision record and an action (risk acceptance, mitigation, monitoring change, product change, or feature retirement). Auditors will accept different tools, but they will not accept missing linkage. ¹

Does MAP-5.2 require external stakeholder councils or formal public consultation?

The requirement calls for regular engagement with relevant actors, but it does not prescribe a single format. Choose channels proportional to risk and reach, then document the cadence and outputs. ¹

What if we only deploy third-party AI and don’t train models ourselves?

You still need engagement and feedback integration because impacts occur in your deployment context. Add third-party escalation paths, change notifications, and QBR coverage so feedback can drive configuration, guardrails, or provider remediation.

How do we handle “positive impacts” without creating busywork?

Treat positive impacts as structured signals: capture them, validate them, and decide whether to scale, document as benefits, or adjust controls to preserve them. Keep the same traceability standard as negative impacts. ¹

Who should own MAP-5.2: Compliance, Product, or Engineering?

Compliance should set the requirement and test evidence, but the operating owner is usually the product or system owner because they can change requirements, releases, and monitoring. Put this in a RACI and avoid shared, unnamed ownership. ¹

Footnotes

1. NIST AI RMF Core

2. NIST AI RMF Core; NIST AI RMF program page

3. NIST AI RMF program page; NIST AI RMF Core